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Guided Wave-Convolutional Neural Network Based Fatigue Crack Diagnosis of Aircraft Structures

Research Center of Structural Health Monitoring and Prognosis, State Key Lab of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
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This paper is an extension version of the conference paper: Xu, L.; Yuan, S.F.; Chen, J.; Bao, Q. Deep learning based fatigue crack diagnosis of aircraft structures. In Proceedings of the 7th Asia-Pacific Workshop on Structural Health Monitoring 2018, Hong Kong, China, 12–15 November 2018.
Sensors 2019, 19(16), 3567; https://doi.org/10.3390/s19163567
Received: 26 June 2019 / Revised: 7 August 2019 / Accepted: 10 August 2019 / Published: 15 August 2019
PDF [2758 KB, uploaded 15 August 2019]

Abstract

Fatigue crack diagnosis (FCD) is of great significance for ensuring safe operation, prolonging service time and reducing maintenance cost in aircrafts and many other safety-critical systems. As a promising method, the guided wave (GW)-based structural health monitoring method has been widely investigated for FCD. However, reliable FCD still meets challenges, because uncertainties in real engineering applications usually cause serious change both to the crack propagation itself and GW monitoring signals. As one of deep learning methods, convolutional neural network (CNN) owns the ability of fusing a large amount of data, extracting high-level feature expressions related to classification, which provides a potential new technology to be applied in the GW-structural health monitoring method for crack evaluation. To address the influence of dispersion on reliable FCD, in this paper, a GW-CNN based FCD method is proposed. In this method, multiple damage indexes (DIs) from multiple GW exciting-acquisition channels are extracted. A CNN is designed and trained to further extract high-level features from the multiple DIs and implement feature fusion for crack evaluation. Fatigue tests on a typical kind of aircraft structure are performed to validate the proposed method. The results show that the proposed method can effectively reduce the influence of uncertainties on FCD, which is promising for real engineering applications.
Keywords: convolutional neural network; guided wave based monitoring; fatigue crack diagnosis; uncertainty; structural health monitoring convolutional neural network; guided wave based monitoring; fatigue crack diagnosis; uncertainty; structural health monitoring
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Xu, L.; Yuan, S.; Chen, J.; Ren, Y. Guided Wave-Convolutional Neural Network Based Fatigue Crack Diagnosis of Aircraft Structures. Sensors 2019, 19, 3567.

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